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Cloudera CDP Data Engineer - Certification Sample Questions:

1. Your Spark application encounters frequent failures during the execution of a specific stage. How can you utilize Spark's persistence mechanisms to improve the application's fault tolerance?

A) Leverage Spark's checkpointing functionality to save application state periodically
B) Persist the RDDs used in the failing stage with MEMORY_ONLY storage level
C) Implement custom retry logic within the failing stage
D) Increase the number of Spark executors to handle potential resource limitations

2. In the context of Spark, what is a potential downside of indiscriminate use of data caching, especially with the MEMORY_AND DISK storage level?

A) It may increase execution time due to overheads from frequent disk 1/0 operations.
B) It can lead to reduced fault tolerance due to reliance on in-memory storage.
C) It enhances data security by storing intermediate results in encrypted form.
D) It can decrease network traffic by reducing the need for data shuffling.

3. In the context of Cloudera's Optimization Framework, what role does data statistics collection play?

A) It reduces the need for data compression
B) It helps the optimizer make informed decisions about data layout and query execution plans
C) It provides metadata for security enforcement
D) It is used to generate more data

4. Which feature in Apache Airflow allows you to retry a data quality check task if it fails initially due to transient issues?

A) catchup
B) retries parameter in the task definition
C) SLAs
D) Branching

5. You're working with a Spark application that processes streaming data in real-time. How does Spark handle persistence in this context?

A) Spark uses the same persistence mechanisms as batch processing for streaming data
B) Spark leverages micro-batching and checkpointing for persistence in streaming applications
C) Streaming data is automatically persisted to HDFS for historical analysis
D) Streaming data cannot be persisted due to its continuous nature

Solutions: